Efficient Orchestration of Virtualization Resource in RAN Based on Chemical Reaction Optimization and Q-Learning

Virtualized network function (VNF) orchestration dynamically deploys network slices, which provides an effective means of customized service provision. To achieve a realistic and comprehensive perspective of the decision process for customized service provision, we propose a virtualized resource orchestration strategy in the radio access network (RAN) of Internet of Things (IoT) based on chemical reaction optimization (CRO). Specifically, we apply particle swarm optimization (PSO), a Gaussian process, random walk model, and $Q$ -learning to enhance the CRO algorithm to quickly obtain the approximate optimal solution for the proposed CRO-based resource orchestration strategy (CROROS). The simulation results show that compared with existing access methods, CROROS can reduce the service rejection rate of a virtualized RAN and improve the utilization rate of network system resources. Compared with other heuristic algorithms [e.g., PSO, genetic algorithm (GA), and CRO], CROROS can accelerate the global approximate optimal solution and improve the approximate fitness of the approximate optimal solution within a specified time.